 From LATE to ATE: A Bayesian approachJournal of Econometrics, 2024, vol. 246, issue 1 Abstract: We develop a Bayesian model that produces a posterior distribution of the marginal treatment effect (MTE) function. The method provides researchers with a principled way to extrapolate from the observed moments using flexible assumptions, thereby allowing researchers to generate plausible ranges of important and potentially policy-relevant quantities of interest. We then use the model to propose a natural decomposition of the posterior variance into “statistical uncertainty,” i.e., variance that stems from the imprecise estimation of the observed moments, and “extrapolation uncertainty,” i.e., variance that stems from uncertainty in how to extrapolate away from the observed moments. We conclude by showing that under our preferred priors, even in an experiment as large as the Oregon Health Insurance Experiment, the main source of uncertainty in the ATE comes from uncertainty in the true values of the observed moments. Keywords: Marginal treatment effects; Bayesian models; Instrumental variables; Compliers; Gaussian process; RCTs (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:246:y:2024:i:1:s030440762400246x DOI: 10.1016/j.jeconom.2024.105895 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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